Data Processing of Deception Detection Based on PCA
Zilong Chen, Dong Liu, Zhiwei Gong, Yunsheng Liu, Ruyu Li
Available Online June 2015.
- https://doi.org/10.2991/meici-15.2015.130How to use a DOI?
- Data processing; Principal component analysis; Deception detection
- Based on the analysis of the difficulty in data processing parameters caused by too many parameters in traditional deception detection, it is presented in this paper to use the method of principal component analysis (PCA) to reduce the number of parameters and the dimension of variables, thus facilitating data processing. Though specific experiments, it is found that the accuracy rate is 90.5% after using PCA, which is not quite different from that obtained before using this method. Moreover, the parameters highly correlated can be well eliminated with PCA, and the redundant data among variables can also be reduced. On the basis of retaining most data, the problems in original data can be effectively solved by using fewer variables, thus fulfilling the purpose of simplifying data and facilitating the analysis of problems. For deception detection experiments with large amounts of data, this is undoubtedly a feasible approach.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Zilong Chen AU - Dong Liu AU - Zhiwei Gong AU - Yunsheng Liu AU - Ruyu Li PY - 2015/06 DA - 2015/06 TI - Data Processing of Deception Detection Based on PCA BT - 2015 International Conference on Management, Education, Information and Control PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/meici-15.2015.130 DO - https://doi.org/10.2991/meici-15.2015.130 ID - Chen2015/06 ER -